{"id":"W2905340466","doi":"10.14288/bctj.v3i1.293","title":"How Accurately do English for Academic Purposes Students use Academic Word List Words?","year":2018,"lang":"en","type":"article","venue":"Spectrum Research Repository (Concordia University)","topic":"Second Language Acquisition and Learning","field":"Psychology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Argumentative; Word (group theory); English for academic purposes; Computer science; Linguistics; Academic writing; Natural language processing; Word list; Error analysis; English as a second language; Artificial intelligence; Psychology; Mathematics education; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004786973,0.0003761511,0.0004696041,0.002633462,0.000506953,0.003105418,0.0006333722,0.000816561,0.001688267],"category_scores_gemma":[0.05443245,0.0002862487,0.0001861265,0.001259413,0.001030672,0.00289113,0.001419352,0.0008134402,0.00193468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003333964,"about_ca_system_score_gemma":0.0003869846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001297363,"about_ca_topic_score_gemma":0.002557678,"domain_scores_codex":[0.9961582,0.001385187,0.0006354157,0.0004946516,0.00107362,0.0002528756],"domain_scores_gemma":[0.9506893,0.02807511,0.01356748,0.001884578,0.004879724,0.000903859],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002522893,0.0002115322,0.8305573,0.0002220106,0.00005493685,0.0004842361,0.0679338,0.0001324748,0.004418341,0.0003678401,0.001392976,0.09397228],"study_design_scores_gemma":[0.00002404093,0.0003093551,0.8909703,0.0003166844,0.00005250456,0.002072135,0.08743731,0.00145708,0.006482716,0.001400225,0.009396477,0.00008123098],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973893,0.0001459289,0.0002585333,0.0001231387,0.00001409307,0.000008712886,0.00009718301,0.00001275301,0.001950278],"genre_scores_gemma":[0.9980236,0.0003166377,0.000448215,0.00007121691,0.00001132125,0.00001477626,0.0001751332,0.00001946056,0.0009197195],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004786973,"threshold_uncertainty_score":0.02531624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09474384852709829,"score_gpt":0.3850065572582029,"score_spread":0.2902627087311047,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}